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Transcript of Sif Project
DATA AGGREGATION IN WSNs
Sensors And Information Fusion 6933
Instructor: Dr.Bill Buckles
Ning ‘Martin’ Xu Kalyan Pathapati Subbu
Shijun Tang
TOPICS
• Introduction• Problem Definition• Clustering• Aggregation• Methodology• Experiments• Performance Analysis & Results• Conclusions
WIRELESS SENSOR NETWORKS
What?• Provide new paradigm
for sensing and disseminating information
• Collection of micro mechanical devices
• Each device capable of wireless communication and signal processing
Features• small size• Robustness• large area coverage• enhanced monitoring
precision
Applications:• Health applications• Environmental and
Structural monitoringDesign Aspects:• Deployment, mobility• Topology, Density• Self configuration• Security
• Environmental monitoring( a group of temperature sensors)
• Similar or even identical readings, minimal difference
DATA AGGREGATION
• Data aggregation is the process of combining similar data from multiple sources to eliminate such redundant transmission and provide fused information to the base station
• Compression can be performed combining multiple data packets into one to reduce overhead of control information (as opposed to data)in the transmission
• Results of arithmetic operations on the data set, such as the average, maximum and minimum, can be sent instead of the original data
• Data In Feature Out form of fusion
PACKAGE
• Hardware– MicaZ mote
• 7.38 MHz Atmel processor with a 128 KB program memory,
• 4 KB RAM and 512 KB non-volatile storage.
• Chipcon SmartRF CC2420, with 2.4GHz frequency
– MTS310 sensor board– Mib520 programming
board
• Software– TinyOS: OS for wireless
sensor networks.– nesC: programming
language for TinyOS.
PROBLEM DEFINITION
Problems:• Energy consumption
– Operating on small batteries : intangible cost to lose data due to battery depletion
• Computational Costs• Storage constraintsSolution for Energy Efficient
operation:• Topology control
– CLUSTERING• Efficient data collection
– AGGREGATION
CLUSTERING
• Grouping of sensors• Distance or proximity • Signal Strength• Logical organizing
• Topology control approach• Load balancing, network
scalability
• Types of clustering• Static: local topology control• Dynamic: changing network
parameters• Single hop and multi hop• Homogeneous and
heterogeneous
HEED- Hybrid Energy Efficient Distributed clustering
• Assumptions:• Sensor quasi-
stationary• Links are symmetric• Energy consumption
non-uniform for all nodes
• Nodes-location unaware
• Processing and communication capability-similar
Algorithm:• Cluster head selection• Factors:
• Primary- residual energy • Secondary-
communication cost • Number of rounds of
iterations • Tentative CHs formed • Final CH until CHprob=1
• Different power levels used for intra and inter-cluster communication
AGGREGATIONWhat?• Process of combining similar data from multiple sources
– Eliminate redundant transmission – Provide fused information to the base station
How?• Sum, Average, Maximum and Minimum
Scenario• Environmental monitoring:
– Group of temperature sensors within the vicinity of one another– Moreover, readings from a single sensor – minimal difference
during a certain period of time in the day.– Primary interest reducing the redundancy coming from
different sensor sources– Average might be sufficient for a small region
METHODOLOGY Exp 1: Clustering and Aggregation iHEEDX• Cluster the nodes according to HEED• Nodes sense temp, light and send to
respective CHs• CH performs aggregation and sends to
Base station
Exp 2: No Clustering and No Aggregation Collection Tree
• Individual nodes sense temp and light
• All nodes directly send to Base station
ENERGY CONSUMPTION• Transmission
– Inter Cluster power level
• Collection Tree all nodes use this power level
• iHEEDX : Only CHs use this power level
– Intra Cluster power level
• All non CH nodes use this power level
• Aggregation
– CH performs Averaging operation on the readings received
– Energy calculated for number of instructions executed by processor
• CREP : Credit point system
CREP System
• The smallest energy can be expressed and well-represented as a multiple of 1 uJ.
• The points in CREP are therefore assigned: The battery capacity is– Battery capacity 23,760,106 points
– other components 70,380 points
– transmission 860 points/packet
– reception 90,000 points
– radio idle state 171 points.
EXPERIMENTAL SETUP
• Nodes placed into three groups – Group 1: 2 and 5, B250
– Group 2: 4 and 7, B245
– Group 3: 1, 3 and 6, B251
• Different places chosen
–Variation in sensed values
Metrics Collected
• Temp, Light
• Overhead_Agg
• Overhead_NoAgg
• Packets_Recvd at BS
• Packets_Recvd_Org at Indv nodes
• Packets_Count sent by Indv nodes
EXPERIMENTAL SETUP (cont’d)
EXPERIMENTAL SETUP (cont’d)
Group1
EXPERIMENTAL SETUP (cont’d)
Group3
EXPERIMENTAL SETUP (cont’d)
Group2
EXPERIMENTAL SETUP (cont’d)
Base Station
• Effect of Data aggregation on sensed data
• Cluster size effect on Energy Consumption and Aggregation
• Overhead comparison for Aggregated and Non-Aggregated scenarios
PERFORMANCE ANALYSIS
EFFECT OF DATA AGGREGATION
• Readings of individual nodes 2 and 5
• Averaged readings from CHs
• Similar data, reduced redundancy, ENERGY SAVED!
0 20 40 60 80 10019.5
20
20.5
21
21.5
Time (seconds)
Tem
pera
ture
(C
)
No.2
No.5Agg
IMPACT OF CLUSTER SIZE
• Cluster size ranging from 1 to 7 nodes
• More the number aggregation performed, ENERGY SAVED!
1 2 3 4 5 6 7540
560
580
600
620
640
660
680
Cluster Size
Ene
rgy
Con
sum
ptio
n (p
oint
s)
Without Aggregation
With Aggregation
OVERHEAD INCURRED
0 100 200 300 400 500 600 7000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 10
5
Number of packets transmitted
Ene
rgy
Con
sum
ptio
n(po
ints
)
With Aggregation
Without Aggregation
• Fewer transmissions, lesser transmission power for intra cluster communication, ENERGY SAVED!
• Joint advantages of clustering and data aggregation
• Experiment in real testbed
• Empirical results confirm energy conservation
Hurdles:• Steep Learning curve - TinyOS
• Hardware issues
Future Work:
• Data aggregation with no prior knowledge
• Outdoor experimentation
CONCLUSIONS
Thank you! Questions?